code: def check_forward(self, x_data, t_data, w_data, samples_data):
      x = chainer.Variable(x_data)
      t = chainer.Variable(t_data)
      w = chainer.Variable(w_data)
      samples = chainer.Variable(samples_data)
  
      y = functions.black_out(x, t, w, samples, self.reduce)
  
      expect_y = numpy.empty((self.batch_size), dtype=numpy.float32)
      for b in range(self.batch_size):
          z = 0
          for i in range(self.n_samples):
              w = self.samples[b, i]
              z += numpy.exp(self.W[w].dot(self.x[b]))
          y0 = self.W[self.t[b]].dot(self.x[b])
          z += numpy.exp(y0)
          l = y0 - numpy.log(z)
          for i in range(self.n_samples):
              w = self.samples[b, i]
              l += numpy.log(
                  1 - numpy.exp(self.W[w].dot(self.x[b])) / z
              )
  
          expect_y[b] = l
  
      if self.reduce == "mean":
          loss = -numpy.sum(expect_y) / self.batch_size
      else:
          loss = -expect_y
  
      testing.assert_allclose(y.data, loss, atol=1.0e-4)
  
file_path: chainer/tests/chainer_tests/functions_tests/loss_tests/test_black_out.py
imports: import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import functions
  from chainer import gradient_check
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import condition
label_desc: Use image node_id 2 for calling the TestBlackOut obj's underlying member method code with example usage: obj.check_forward(x_data, t_data, w_data, samples_data) without return types